The basic function of a communication system is to send information from a source that generates the information to one or more destinations. In a radio communication system, a number of obstacles must be overcome to successfully transmit and receive information including channel distortion which may cause, for example, intersymbol interference (ISI) and additive noise. The receiver must compensate for the effects of channel distortion and the resulting intersymbol interference. This is usually accomplished by means of an equalizer/detector at the receiver.
A number of different equalization schemes have been used in the past to eliminate or minimize intersymbol interference. The most commonly used approaches include decision feedback equalization (DFE) and maximum likelihood sequence estimation (MLSE). In a maximum likelihood sequence estimation equalization scheme, a detector produces the most probable symbol sequence for the given received sample sequence . An algorithm for implementing maximum likelihood sequence detection is the Viterbi algorithm, which was originally devised for decoding convolutional codes. The application of the Viterbi algorithm to the problem of sequence detection in the presence of ISI is described by Gottfried Ungerboeck in "Adaptive Maximum Likelihood Receiver for Carrier Modulated Data Transmission Systems," IEEE Transactions on Communications, Volume COM-22, #5, May 1974. This MLSE approach employs a matched filter followed by a MLSE algorithm and an auxiliary channel estimation scheme.
A major drawback of using maximum likelihood sequence detection for communications channels which may experience intersymbol interference is that the computational complexity grows exponentially as a function of the span of the intersymbol interference and the number of interfering users. Consequently, maximum likelihood sequence detection is practical only for single user signals where the intersymbol interference spans only a few symbols. Therefore, there is great interest in finding new approaches to sequence estimation which reduce the computational complexity associated with maximum likelihood sequence detection.